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@amankharwal
Created Sep 29, 2020
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y_pred = np.array(clf.predict(x_test))
conf_mat = pd.DataFrame(confusion_matrix(y_test, y_pred),
columns=["Pred.Negative", "Pred.Positive"],
index=['Act.Negative', "Act.Positive"])
tn, fp, fn, tp = confusion_matrix(y_test, y_pred).ravel()
accuracy = round((tn+tp)/(tn+fp+fn+tp), 4)
print(conf_mat)
print(f'\n Accuracy = {round(100*accuracy, 2)}%')
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